Causal Combinatorial Factorization Machines for Set-wise Recommendation. But due to the unforeseen COVID-19 virus pandemic around the world, the PAKDD 2020 conference was held online instead. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all … From those, 135 papers have been accepted, which means an acceptance rate of 21.5%. POSTERS [32] Fast Triangle Core Decomposition for Mining Large Graphs; Ryan Rossi [43] Finding Better Topics: Features, Priors and Constraints; Xiaona Wu, Jia Zeng, Jianfeng Yan, Xiaosheng Liu [46] … Thus, the global acceptance rate was about 28%. A paper accepted for PAKDD 2021. Hady Lauw, Raymond Wong, Alexandros Ntoulas Evaluation: The official evaluation metric for the shared task is weighted-average F1 score System description paper: All team/participants will be invited to submit their models as short papers to be included in the proceedings.Based on the reviewers' comments, we will decide which papers to be accepted. }, [16] Enabling Hierarchical Dirichlet Processes to work better for short texts at large scaleKhai Mai, Sang Mai, Anh Nguyen, Linh Ngo, Khoat Than, [21] A Greedy Algorithm to Construct L1 Graph with Ranked DictionaryShuchu Han, Hong Qin, [24] Leveraging Emotional Consistency for Semi-Supervised Sentiment ClassificationMinh Luan Nguyen, [50] LBMF: Log-Bilinear Matrix Factorization for Recommender SystemsYunhui Guo, Xin Wang, Congfu Xu, [60] An Expert-in-the-loop Paradigm for Learning Medical Image GroupingXuan Guo, Qi Yu, Rui Li, Cecilia Ovesdotter Alm, Cara Calvelli, Pengcheng Shi, Anne Haake, [78] Denoising Time Series By Way of A Flexible Model For Phase Space ReconstructionMinhzul Islam Sk, Arunava Banerjee, [79] Deep Feature Extraction from Trajectories for Transportation Mode EstimationYuki Endo, Hiroyuki Toda, Kyosuke Nishida, Akihisa Kawanobe, [83] Unsupervised Parameter Estimation for One-Class Support Vector MachinesZahra Ghafoori, Sutharshan Rajasegarar Sarah M. Erfani, Shanika Karunasekera, Christopher Leckie, [89] Adaptive Seeding for Gaussian Mixture ModelsJohannes Blomer, Kathrin Bujna, [91] FastStep: Scalable Boolean Matrix DecompositionMiguel Araujo, Pedro Ribeiro, Christos Faloutsos, [93] Frequent Pattern Outlier Detection without Exhaustive MiningArnaud Soulet, Arnaud Giacometti, [130] Social Group Based Video Recommendation Addressing the Cold-Start ProblemChunfeng Yang, Yipeng Zhou, Liang Chen, Xiaopeng Zhang, Dah ming Chiu, [131] Will I Win Your Favor? This year, 458 papers were submitted. At least one author of each accepted paper must register and participate in the workshop to present the paper. If required supplementary material may be submitted as a separate PDF file, but reviewers are not obligated to consider this, and your manuscript should therefore stand on its own merits without any supplementary material. Conference Venue; Conference Rooms; Transportation; Accommodation; FURTHER INFORMATION. The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. Each oral presentation is 17+3 minutes. The Program Committee members were deeply involved in a highly engaging selection process with discussions among reviewers. Submitting a paper to the workshop means that if the paper is accepted, at least one author should attend the workshop to present the paper. As a result, … 12/4 – Due to the large number of proposals, the announcement of accepted proposals will be delayed until Dec 11 th, 2020. Before submitting your paper, please carefully read and agree with the PAKDD Paper Submission Policy and No-Show Policy: https://pakdd.org/policies/ Note that we had a large number of workshops with significant overlap in scope. Conference stats are visualized below for a straightforward comparison. The submitted papers must not be previously published anywhere, and must not be under consideration by any other conferences or journal during the PAKDD review process. Best Paper Awards (NEW) Travel Awards (NEW) Contest Awards (NEW) REGISTRATION. } Nizar Bouguila. EXPLORE TAIWAN. At least one author of each accepted paper must register and participate in the workshop to present the paper. PAKDD09-0105 : Negative Encoding Length as a Subjective Interestingness Measure for … Each paper was rigorously reviewed by at least two program committee members, discussed by the reviewers under the supervision of an area chair, and judged by the program committee chairs. The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of knowledge discovery and data mining (KDD). At least one author of each accepted paper must register and participate in the workshop to present the paper. Submitting a paper to the conference means that if the paper was accepted, at least one author will complete the regular registration and attend the conference to present the paper. The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of knowledge discovery and data mining (KDD). Papers that do not comply with the Submission Policy will be rejected without review. Accepted Papers. Accepted Papers PAKDD2008 received 312 submissions from 34 countries and regions in Asia, Australasia, North America, South America, Europe and Africa. Submitting a paper to the conference means that if the paper were accepted, at least one author will attend the conference to present the paper. The conference went online This year, the PAKDD 2020 conference was planned to be held in Singapore. But due to the unforeseen COVID-19 virus pandemic around the world, the PAKDD 2020 conference was held online instead. However, if authors would like their papers to be considered for the LNCS/LNAI post Proceedings of PAKDD Workshops published by Springer (indexed by EI Compendex, ISI Proceedings, and Scopus), please indicate this at the time of … Registration; Participant Form; CONFERENCE SITE. Code Reproducibility: To improve code reproducibility and transparency in … Become a Sponsor: Sponsorship exposes your brand to highly qualified attendees, funds our diversity and student grants, supports open access to our conference content, and keeps USENIX conferences affordable. PAKDD09-0093: A Statistical Approach for Binary Vectors Modeling and Clustering. For no-show authors, their papers will not be included in the proceedings. Formatting template: http://www.springer.de/comp/lncs/authors.html, Submission Site: https://cmt3.research.microsoft.com/PAKDD2020, If you have any questions, please feel free to contact us at pakdd2020@gmail.com These can be submitted to PAKDD provided that the submitted paper’s title and abstract are different from the one appearing on arXiv. if (item) { Our paper on estimation of causal effects of combinatorial treatments was accepted for PAKDD 2021: Akira Tanimoto, Tomoya Sakai, Takashi Takenouchi, Hisashi Kashima. if the paper was accepted, at least one author will attend the conference to present the paper. | Regular papers | Short papers | Regular papers. Accepted paper at PAKDD 2020 Main Navigation. The submitted papers went through a rigorous reviewing process. The submitted papers went through a rigorous reviewing process. 11.12.15: Full paper accepted in PAKDD 2016. PAKDD 2020 welcomes high-quality, original and previously unpublished submissions in the theory, practice, and applications on all aspects of knowledge discovery and data mining. The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of knowledge discovery and data mining (KDD). June 19 th – June 25 th, 2021. 01/01/2014, Accepted Papers have been announced. In general the quality of the papers at PAKDD conferences is good. Accepted Papers; Research Track Papers - Oral. If there is a large disagreement, the area chair and/or PC co-chairs provided an additional review. Among those, 45 papers were accepted as long papers and 84 as short papers. AI Ops competition 2020 on disk failure prediction, cloud computing, binary-classification, focal loss function, voting-based strategy, deep learning, LGB model, intelligent operation and maintenance, regression labelling, greedy postprocessing, anomaly … Workshop call for papers: Nov 2, 2020: Paper submission deadline: Refer workshop site: Workshop author notification: Feb 22, 2021 : Workshop camera-ready due: Mar 8, 2021 *All deadlines are 23:59 Pacific Standard Time (PST) Call for Workshops. An exception to this rule applies to manuscripts that were published in arXiv not later than 25 October 2019, i.e., at least a month prior to PAKDD’s submission deadline. The workshop papers will be included in a LNCS/LNAI post Proceedings of PAKDD Workshops published by Springer. 1. Accepted Papers Accepted Percentage/Acceptance Rate; We are working hard to collect and update the acceptance rate details of the conferences for recent years. item.className=(item.className=='hidden')? Authors are strongly encouraged to use Springer LNCS/LNAI manuscript submission guidelines for their initial submissions. 15.11.15: My PhD thesis gets nominated at XRCI 2016 for best thesis award. For no-show authors, their papers will not be included in the proceedings. The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and … Submissions must have all details identifying the author(s) removed from the original manuscript (including the supplementary files, if any), and the author(s) should refer to their own prior work in the third person and include all relevant citations. Each paper was rigorously reviewed by at least two program committee members, discussed by the reviewers under the supervision of an area chair, and judged by the program committee chairs. At least one author of each accepted paper must register and participate in the workshop to present the paper. PAKDD 2016 Poster. Accepted paper at PAKDD 2020. The submitted papers must not be previously published anywhere, and must not be under consideration by any other conferences or journal during the PAKDD review process. Conference Scope The … Accepted Papers. The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the areas of knowledge discovery and data mining (KDD). Submitting a paper to the conference means that if the paper was accepted, at least one author will complete the regular registration and attend the conference to present the paper. This year, the PAKDD 2020 conference was planned to be held in Singapore. The workshop papers will be included in LNCS/LNAI post Proceedings of PAKDD Workshops published by Springer . ^All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations. Call For Papers The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. By using both terms (words) and biterms to represent documents, bag-of-biterms (BoB) provides significant benefits: (1) it naturally lengthens documents and thus helps us reduce bad effects … List Of Accepted Papers. Submitted papers will be evaluated by at least three members of the international program committee. Updated April 8, 2020; June 1, 2020 – the above dates have all been updated to reflect the new scheduled dates EMNLP in its online format, and to account for recent events. The conference went online. 12/23/2013, Camera Ready Submission Guidelines have been announced. This year, 113 papers among the 338 submissions have been accepted (with an acceptance rate of 33.43%). var item = document.getElementById(divID); Felix Borutta, Daniyal Kazempour, Felix Marty, … Tracking Customer Behavior Changes via Preference ModelingLing Luo, Bin Li, Irena Koprinska, Shlomo Berkovsky, Fang Chen, [111] Constraint Based Subspace Clustering for High Dimensional Uncertain DataXianChao Zhang, Lu Gao, Hong Yu, [127] Personal Credit Profiling via Latent User Behavior Dimensions on Social MediaGuangming Guo, Enhong Chen, Feida Zhu, Le Wu, Qi Liu, [129] Early-Stage Event Prediction for Longitudinal DataMahtab Jahanbani Fard, Sanjay Chawla, Chandan Reddy, [138] Flexible Transfer Learning Framework for Bayesian OptimisationTinu Joy, Santu Rana, Sunil Gupta, Svetha Venkatesh, [139] Sparse Adaptive Multi-Hyperplane MachineKhanh Nguyen, Trung Le, Vu Nguyen, Dinh Phung, [145] Query-focused Multi-document Summarization based on Concept ImportanceHaiTao Zheng, Jimin Guo, [159] Locally Weighted Ensemble Learning for RegressionQinghua Hu, Man Yu, [163] Shot Boundary Detection using Multi-instance Incremental and Decremental One-Class Support Vector MachinesHanhe Lin, Jeremiah Deng, Brendon Woodford, [168] A Rule based Open Information Extraction Method Using Cascaded Finite-State TransducerHailun Lin, Peng Zhang, [174] A Simple Unlearning Framework for Online Learning under Concept DriftsSheng-Chi You, Hsuan-Tien Lin, [178] FeRoSA: A Faceted Recommendation System for Scientific ArticlesTanmoy Chakraborty, Amrith Krishna, Mayank Singh, Niloy Ganguly, Pawan Goyal, Animesh Mukherjee, [179] Towards Automatic Generation Of MetafeaturesFabio Pinto, Carlos Soares, Joao Mendes-Moreira, [187] Predicting Unknown Interactions between Known Drugs and Targets via Matrix CompletionQing Liao, Naiyang Guan, Chengkun Wu, Qian Zhang, [192] Analyzing Similarities of Datasets using a Pattern Set KernelIbrahim A, P Sastry, Shivakumar Sastry, [201] Hash Learning with Convolutional Neural Networks for Semantic Based Image RetrievalJinma Guo, Jianmin Li, Shifeng Zhang, [203] Enhanced SVD for Collaborative FilteringXin Guan, Chang-Tsun Li, Yu Guan, [210] Reliable Confidence Predictions Using Conformal PredictionHenrik Linusson, Ulf Johansson, Henrik Bostrom, Tuve Lofstrom, [223] A Clustering-based Framework for Incrementally Repairing Entity ResolutionQing Wang, Jingyi Gao, Peter Christen, [226] Parallel Discord DiscoveryTian Huang, Mengyun Liu, Xinyang Li, Yishu Mao, Yongxin Zhu, [231] Dboost: a fast algorithm for DBSCAN-based clustering on high dimensional dataYuxiao Zhang, Xiaorong Wang, Binyang Li, Wei Chen, Tengjiao Wang, and Kai Lei, [242] Mirror on the Wall: Finding Similar Questions using Deep Semantic Topic ModelingArpita Das, Manish Shrivastava, Manoj Chinnakotla, [245] An Efficient Dynamic Programming Algorithm for STR-IC-STR-IC-LCS ProblemXiaodong Wang, [250] Computing Hierarchical Summary of the Data StreamsZubair Shah, Abdun Naser Mahmood, Michael Barlow, [255] Distributed Sequential Pattern Mining in Large Scale Uncertain DatabasesJiaqi Ge, Yuni Xia, [256] The effect on accuracy of tweet sample size for hashtag segmentation dictionary constructionLaurence Park, Glenn Stone, [260] WANBIA-C trick for Effective Pre-conditioning of Logistic Regression with Mean-Square-ErrorNayyar A. Zaidi, Francois Petitijean, Geoff I. Webb, [263] DeepCare: A Deep Dynamic Memory Model for Predictive MedicineTrang Pham, Truyen Tran, Dinh Phung, Svetha Venkatesh, [269] Fast and Semantic Measurements on Collaborative Tagging QualityYuqing Sun, [276] Active Distance-Based Clustering using K-medoidsMehrdad Ghadiri, Amin Aghaee, Mahdieh Soleymani Baghshah, [280] Transfer-Learning based Model for Reciprocal RecommendationChia-Hsin Ting, Shou-De Lin, [285] Social Identity Link across Incomplete Social Information Sources Using Anchor Link ExpansionYuxiang Zhang, Lulu wang, Xiaoli Li, Chunjing Xiao, [296] Joint Classification with Heterogeneous labels using random walk with dynamic label propagationYongxin Liao, Shenxi Yuan, Jian Chen, Qingyao Wu, Bin Li, [304] Incorporating Heterogeneous Information for Mashup Discovery with Consistent RegularizationYao Wan, Liang Chen, Qi Yu, Tingting Liang, Jian Wu, Website created by Alexandr Shirokov | Developed and maintained by David TJ HuangDepartment of Computer Science, The University of Auckland Header photo credit: Sids1/CC BY. The submitted papers must not be previously published anywhere and must not be under consideration by any other conference or journal during the PAKDD review process. 06.02.2020 Authors. 06.02.2020 Authors. well-accepted problem in this industry. Smart Tourism Taiwan. One of our papers get accepted in PAKDD 2021 Two of our papers get accepted in the WINE Workshop on Game Theory in Blockchain 2020; MLL student Sankarshan Damle banged the prestigious Ripple-IIITH PhD fellowship for pursuing PhD in blockchain related areas. Topics of relevance for the conference include, but not limited to, the following: Paper submission must be in English. Attendees are required to register at PAKDD 2010 website. All papers will be double-blind reviewed by the Program Committee on the basis of technical quality, relevance to data mining, originality, significance, and clarity. Submitting a paper to the conference means that if the paper was accepted, at least one author will complete the regular registration and attend the conference to present the paper. Northern Taiwan. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD related areas, including … 11/08/20 CVPR 2021 will now take place virtually. Authors: Please be sure to see the Poster Presentation Instructions as you prepare for KDD 2018. 2. Two of our papers got accepted in ACML 2020; Our lab members secure PhD admits with coveted … PAKDD 2014 APP. PAKDD 2016 Poster. Submitted papers will be evaluated by at least three members of the international program committee. It provides an international forum for researchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intellige… 3. Each submitted paper should include an abstract up to 200 words and be no longer than 12 single-spaced pages with 10pt font size (including references, appendices, etc.). Accepted Papers. The workshop papers will be included in a LNCS/LNAI post Proceedings of PAKDD Workshops published by Springer . The submitted papers must not be previously published anywhere and must not be under consideration by any other conference or journal during the PAKDD review process. In this paper, we show a very simple approach (namely, bag-of-biterms) that helps statistical models such as Hierarchical Dirichlet Pro- cesses (HDP) to work well with short texts. Southwestern Taiwan. Conference Sponsorship. To learn more, please contact the … Our paper “MemMAP: Compact and Generalizable Meta-LSTM Models for Memory Access Prediction” has been accepted as a full paper at the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2020. We will keep you posted on the same. The submitted papers must not be previously published anywhere and must not be under consideration by any other conference or journal during the PAKDD review process. Predicting the Success of Altruistic RequestsHsun-Ping Hsieh, Cheng-Te Li, [133] Sparse Logistic Regression with Logical FeaturesYuan Zou, Teemu Roos, [136] Optimal Training and Efficient Model Selection for Parameterized Large Margin LearningYuxun Zhou, Claire Baek, Costas J. Spanos, [143] Classification with Quantification for Air Quality MonitoringSanad Al Maskari, Eve B’elisle, Xue Li, S’ebastien Le Dig, Amin Nawahda, Jiang Zhong, [146] Predicting Post-Operative Visual Acuity for LASIK SurgeriesManish Gupta, Prashant Gupta, Pravin Vaddavalli, Asra Fatima, [147] Clustering Large Attributed Graphs with Multiple Sparse AnnotationsJianping Cao, Senzhang Wang, Hui Wang, Feiyue Wang, Philip Yu, [167] Effective Local Metric Learning for Water Pipe AssessmentMojgan Ghanavati, Raymond K. Wong, Fang Chen, Yang Wang, Simon Fong, [171] Modeling Adversarial Learning as Nested Stackelberg GamesYan Zhou, Murat Kantarcioglu, [173] Discovering the Network Backbone from Traffic Activity DataVenkata Rama Kiran Garimella, Sanjay Chawla, Aristides Gionis, Dominic Tsang, [175] OCEAN:Fast Discovery of High Utility Occupancy ItemsetsBilong Shen, Zhaoduo Wen, Ying Zhao, Dongliang Zhou, Weimin Zheng, [181] Dual Similarity Regularization for RecommendationJing Zheng, Jian Liu, Chuan Shi, Fuzhen Zhuang, Bin Wu, [182] Hybrid Sampling with Bagging for Class Imbalance LearningYiu-ming Cheung, Yang Lu, [183] A Nonlinear Label Compression and Transformation Method for Multi-Label Classification using AutoencodersJorg Wicker, Andrey Tyukin, Stefan Kramer, [191] Link Prediction in Schema-Rich Heterogeneous Information NetworkXiaohuan Cao, Yuyan Zheng, Chuan Shi, Bin Wu, [208] A Fast and Complete Enumeration of Pseudo-Cliques for Large GraphsHongjie Zhai, Makoto Haraguchi, Yoshiaki Okubo, Etsuji Tomita, [209] Collaborative Deep Ranking: a Hybrid Pair-wise Algorithm with Implicit FeedbackHaochao Ying, Liang Chen, Yuwen Xiong, Jian Wu, [214] Dynamic Grouped Mixture Models for Intermittent Multivariate Sensor DataNaoya Takeishi, Takehisa Yairi, Naoki Nishimura, Yuta Nakajima, Noboru Takata, [220] Significant Pattern Mining with Confounding VariablesAika Terada, Dave duVerle, Koji Tsuda, [229] Robust Multi-View Manifold Ranking for Image RetrievalJun Wu, Jianbo Yuan, Jiebo Luo, [234] Exploring Heterogeneous Product Networks for Discovering Collective Marketing Hyping BehaviorQinzhe Zhang, Guodong Long, Peng Zhang, Chengqi Zhang, [239] Toxicity Prediction in Cancer Using Multiple Instance Learning in a Multi-Task FrameworkCheng Li, Sunil Gupta, Santu Rana, Wei Luo, Svetha Venkatesh, David Ashley, Dinh Phung, [249] Efficient Page-Level Data Extraction Via Schema Verification Chia-Hui Chang, [253] Building Compact Lexicons for Cross-Domain SMT by mining near-optimal Pattern SetsPankaj Singh, Ashish Kulkarni, Himanshu Ojha, Vishwajeet Kumar, Ganesh Ramakrishnan, [261] Image Representation Optimization Based on Locally Aggregated DescriptorsShijiang Chen, Shijiang Chen, Guiguang Ding, Yuchen Guo, [265] Bayesian Group Feature Selection for Support Vector Learning MachinesChangde Du, Changying Du, Shandian Zhe, Ali Luo, Qing He, Guoping Long, [284] Active Learning Based Entity Resolution Using Markov LogicJeffrey Fisher, Peter Christen, Qing Wang, [305] Grade Prediction with Course and Student Specific ModelsAgoritsa Polyzou, George Karypis, [2] Automated Setting of Bus Schedule Coverage using Unsupervised Machine LearningJihed Khiari, Luis Moreira-Matias, Vitor Cerqueira, Oded Cats, [3] Multi-Hypergraph Consistent Sparse Coding for Image Data ClusteringXiaodong Feng, Sen Wu, Wenjun Zhou, Zhiwei Tang, [27] Linear Upper Confidence Bound Algorithm for Contextual Bandit Problem with Piled RewardsKuan-Hao Huang, Hsuan-Tien Lin, [32] A Precise and Robust Clustering Approach using Homophilic Degrees of Graph KernelHaolin Yang, Deli Zhao, Lele Cao, Fuchun Sun, [40] Indoor Positioning System for Smart Homes based on Decision Trees and Passive RFIDFrederic Bergeron, Kevin Bouchard, Sylvain Giroux, SEbastien Gaboury, Bruno Bouchard, [44] Matrices, Compression, Learning Curves: formulation, and GROUPNTEACH algorithmsBryan Hooi, Hyun Ah Song, Evangelos Papalexakis, Rakesh Agrawal, Christos Faloutsos, [48] Privacy Aware K-Means Clustering with High UtilityThanh Nguyen, Sunil Gupta, Santu Rana, Svetha Venkatesh, [52] Forest CERN: A New Decision Forest Building TechniqueMd Nasim Adnan, Md Zahidul Islam, [53] Secure k-NN Query on Encrypted Cloud Data with Limited Key-disclosure and Offline Data OwnerYouwen Zhu, Zhikuan Wang, Yue Zhang, [54] Incremental Hierarchical Clustering of Stochastic Patterns for Symbolic Data AnalysisXin Xu, Jiaheng Lu, Wei Wang, [56] Cost-Minimizing Team Hires with Participation ConstraintMengjie Wan, Jianbin Huang, Ke Liu, Heli Sun, Zhou Yang, [62] Hashing-based Distributed Multi-party Blocking for Privacy-preserving Record LinkageThilina Ranbaduge, Dinusha Vatsalan, Peter Christen, Vassilios Verykios, [63] Unsupervised and Semi-Supervised Dimensionality Reduction with Self-Organizing Incremental Neural Network and Graph Similarity ConstraintsZhiyang Xiang, Zhu Xiao, Yourong Huang, Dong Wang, Chen Wenjie, [64] Reusing Extracted Knowledge in Genetic Programming to Solve Complex Texture Image Classification ProblemsMuhammad Iqbal, Mengjie Zhang, [68] TrafficWatch: Real-time Traffic Incident Detection and Monitoring Using Social MediaHoang Nguyen, Wei Liu, Paul Rivera, Fang Chen, [76] An Empirical Study on Hybrid Recommender System with Implicit FeedbackSunhwan Lee, Anca Chandra, Divyesh Jadav, [80] Cross-View Feature Hashing for Image RetrievalWei Wu, Bin Li, Ling Chen, Chengqi Zhang, [86] Online Learning for Accurate Real-Time Map Matching without Human LabelingBiwei Liang, Tengjiao Wang, Wei Chen, Hongyan Li, [94] Ensembles of Interesting Subgroups for Discovering High Potential EmployeesGirish Palshikar, Kuleshwar Sahu, Rajiv Srivastava, [107] Who will be Affected by Supermarket Health Programs?
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